a8c866c32b
* add ppl benchmark * add license * add readme * add dataset argument * add dataset usage * fixed low bit args * correct result * fix terminal display * fix ppl update * enable fp16 fp32 bf16 * format the desc * fix model_kwargs * add more readme
84 lines
3.2 KiB
Python
84 lines
3.2 KiB
Python
#
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# Copyright 2016 The BigDL Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import intel_extension_for_pytorch as ipex
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import numpy as np
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import torch
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from tqdm import tqdm
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from transformers import AutoTokenizer
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from bigdl.llm.transformers import AutoModelForCausalLM
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class PPL:
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def __init__(self):
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self.nll = 0
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self.cnt = 0
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def __call__(self, all_logits, labels):
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'''
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all_logits [seq_length, vocab_size]
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labels [seq_length]
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'''
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seq_length = all_logits.shape[0]
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for i in range(0, seq_length - 1):
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logits = all_logits[i, :]
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max_logit = np.amax(logits)
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sum_exp = np.sum(np.exp(logits - max_logit))
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# logits at time-step i is for predicting token at time-step (i+1)
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next_tok = labels[i + 1]
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log_softmax_of_tok = (logits[next_tok] - max_logit) - np.log(sum_exp)
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self.nll += -log_softmax_of_tok
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self.cnt += 1
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return np.exp(self.nll / self.cnt)
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def result(self):
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return np.exp(self.nll / self.cnt)
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def __str__(self):
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return f"PPL: {np.exp(self.nll / self.cnt):.3f}"
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class BigDLPPL:
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def __init__(self, model_path, device, **model_kwargs) -> None:
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model_kwargs['trust_remote_code'] = model_kwargs.get('trust_remote_code', True)
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model_kwargs['optimize_model'] = model_kwargs.get('optimize_model', True)
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self.device = device
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self.tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
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self.model = AutoModelForCausalLM.from_pretrained(model_path, **model_kwargs)
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if 'xpu' in device:
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import intel_extension_for_pytorch as ipex
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self.model.to(device)
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self.ppl_evaluator = PPL()
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def perplexity_hf(self, text):
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inputs = self.tokenizer('\n\n'.join(text), return_tensors='pt').to(self.device)
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input_ids = inputs['input_ids']
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# attention_mask = inputs['attention_mask']
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progress_bar = tqdm(range(0, input_ids.shape[1], 512))
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for i0 in progress_bar:
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input_ids_chunks = input_ids[:, i0:(i0+512)]
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input_ids_chunks[:, 0] = 1
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with torch.no_grad():
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result = self.model.forward(input_ids_chunks, labels = input_ids_chunks, return_dict=True)
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#print(f"ppl = {torch.exp(result.loss)}")
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seq_len = result.logits.shape[1]
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data = result.logits
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data = data.to('cpu')
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input_ids_chunks = input_ids_chunks.to('cpu')
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self.ppl_evaluator(data.numpy()[0, seq_len//2:, :], input_ids_chunks.numpy()[0, seq_len//2:])
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progress_bar.set_description(f"{self.ppl_evaluator}")
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return self.ppl_evaluator.result()
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